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androidtwitterapp

An android app to display twitter trend analysis results

backgroundaudiorecord

c-programming

C++ programming assignments

ccv_facedetection

Face detection using CCV, using wesbos code

chatup-server

chromeextensions

Chrome extensions - simple js experiments

dsa_assignments

Algorithms Assignments

excelproject

Replicating excel-sheet behaviour in console. A C++ course project

guesscolor

An HTML5 game I designed for an Economics Professor in Rutgers University

intrusiondetect

To detect intrusion in networks using string matching algorithms

invertedindex

minijarvis

Live Annotations with Google Glass

mjfaceserver

A faster version of the earlier Mini Jarvis Face Server, in which detection and recognition is done using Eigenvector algorithm, JAI and JJIL libraries.

nodeslash

nodetutorials

scalacoursera

Solutions coded for the Coursera course 'Functional Programming Principles in Scala

seeker---a-search-engine

An implementation of search engine using Nutch and Hadoop

stockprediction

Application to predict stock values by analyzing previous values using Bayesian curve fitting

swagger-scala-module

Swagger support for scala

twitterdataanalysis

In this project we did sentimental analysis on data collected from the social media, Twitter and predicted the current trend. The data can be tweets, quoted tweets and the favorites for a tweet (the number of times a tweet has been liked). Data was collected for a pair of keywords using the Twitter Search API. The collected tweets are then classified as positive, negative, neutral or junk based on the sentimental analysis of the text in the tweet/quoted tweet (favorites are considered as positive). Based on this classification it is possible to predict which among the pair of keywords is more popular. The prediction is under the assumption that more positive and neutral responses are there for a keyword, more trending it is with the public. An Android app was created to display data analysis results for a pair of keywords The accuracy of prediction was examined by predicting the outcome of November 5th Governor Elections in New Jersey using keywords Barbara Buono and Chris Christie.

virtualdiary

In this project, the personal data is collected from Facebook, Foursquare, Twitter and Google Calendar using Neemi Data Extraction Tool. The data is saved in a NoSQL open source database called mongoDB. To do sentimental analysis on each tweet/status, we used a third party library called Alchemy API. The sentiments of all statuses in a day were analyzed to predict the mood.

wordcount

A sampe wordcount mapreduce program